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    Physics-informed AI · Data-efficient by design

    Great AI
    does not need
    big data.

    Where sustainability meets innovation and data efficiency unlocks unprecedented potential.

    Explore Platform
    AI that solves your problem with
    less data

    Born out of research at

    Chalmers University of TechnologyChalmers University
    University of GothenburgGothenburg University

    In partnership with

    NVIDIA
    BioVentureHub
    iflai

    Based at AstraZeneca R&D BioVentureHub, Mölndal, Sweden

    EU Flag

    Supported by ERC

    About us

    What makes iflai different?

    We embed decades of physics research directly into our AI architectures, making them inherently data-efficient, energy-conscious, and precise. All models are proprietary and built from the ground up.

    Decades
    Of cumulative research

    Physics-informed AI

    Our proprietary models encode real-world physical knowledge, letting them learn hyperefficiently from minimal examples.

    >99.9%
    Accuracy

    Limited data? No problem

    Achieve >99.9% accuracy with as few as 1–5 labeled datapoints. No big datasets needed.

    95%
    Less compute

    Sustainable by design

    Up to 95% less compute, energy, and cost. Models that understand physics don't need brute force.

    3+
    Industries

    Proprietary & adaptable

    All models are built from the ground up. Adaptable solutions across medtech, manufacturing, security, and beyond.

    The iflai Platform

    Immediate impact across industries

    Our technology is already revolutionizing AI across a wide range of industries, empowering businesses to innovate faster, smarter, and more sustainably.

    Cell AnalysisLabVIEW / MCPPhenotypic Screening

    AI for Medtech

    Data-efficient AI for medicinal screening, delivered as standalone workflows or integrated into lab stacks through Docker, ONNX, LabVIEW, and MCP where relevant.

    Learn more
    Anomaly DetectionDocker / ONNXEdge Integration

    AI for Manufacturing

    Automatic anomaly detection from single examples, delivered as operator workflows, Docker or ONNX services, or on-edge deployments when lines need machine-side control.

    Learn more
    Threat DetectionDocker / MCPArchive Search

    AI for Security

    Threat detection and retrieval delivered as secure analyst workflows or integrated into investigation stacks through Docker, ONNX, and MCP.

    Learn more

    How it works

    Two paths to production-ready AI

    Whether you need a custom model for microscopy, manufacturing, or security, or a large-scale foundation model, we get you from raw data to deployment with minimal overhead.

    Delivery Mode 01

    Standalone Products

    Use the workflow as an operator, scientist, or analyst-facing product with a clear pilot package and production path.

    Pilot PackageProduction License
    Delivery Mode 02

    Enterprise Integrations

    Most products can also be delivered as Dockerized services, ONNX-friendly deployment targets, or secure on-prem components inside existing software stacks.

    DockerONNXOn-Prem
    Delivery Mode 03

    Agent-Ready Interfaces

    Where teams already run AI agents, we can expose the model layer through MCP and domain-specific connectors such as LabVIEW handoffs where relevant.

    MCPLabVIEWWorkflow APIs

    Custom AI Models

    · Microscopy · Manufacturing · Security
    Step 01

    Sample

    We collect a minimal amount of data. A handful of cells, a few defects, or a small set of threat objects. When needed, our active samplers intelligently select the most informative examples for you.

    Active SamplingMinimal AnnotationDomain-Agnostic
    Step 02

    Train

    Our physics-informed, inductive-bias-aware models learn from your data to perform detection, localization, segmentation, classification, quantification, tracking, video analysis, or multidimensional analysis. Often all at once.

    Detection & LocalizationSegmentationClassificationTracking & Video
    Step 03

    Deploy

    Receive your production-ready model as a standalone workflow, a Dockerized or ONNX deployment, an MCP-connected component for existing AI agents, or optimized on-edge integration when machine-side latency matters.

    Standalone WorkflowDocker / ONNXMCP IntegrationEdge Integration

    Foundation Models

    · Enterprise-scale, purpose-built
    Step 01

    Integrate

    We meet, understand your requirements, and establish a secure data pipeline. Fully aligned with your cybersecurity governance and compliance standards.

    Security-FirstCustom PipelineGovernance-Compliant
    Step 02

    Train

    Our inductive-bias-aware architecture trains on only the most relevant data, selected by active samplers. Maximizing performance while minimizing data volume, training cost, and energy consumption.

    Active SamplingData-EfficientLow Carbon Footprint
    Step 03

    Deploy

    Your trained foundation model ships as ready weights, a secure service, or a reusable inference layer inside existing screening, lab, and agent workflows.

    Ready WeightsOn-PremisesDocker / ONNXMCP Interfaces

    Publications

    Our latest research

    A selection of our peer-reviewed publications across Nature, ACS, and more. The science behind our physics-informed AI.

    Beyond Off-the-Shelf

    Got a problem but no data?

    No dataset, no infrastructure, no problem. We build custom AI solutions from scratch, from data collection to model deployment. If it can be solved with AI, we'll find a way.

    Let's Talk